Guided Ultrasound Imaging for Point-Of-Care Staging of Medical Conditions

The present disclosure relates generally to ultrasound imaging. In particular, an ultrasound system provides guidance to acquire ultrasound images of multiple views, automatically extracts features from the ultrasound images, assigns sub-scores to the features, and determines a staging value of a medical condition of the patient.

Physicians use many different medical diagnostic systems and tools to monitor a subject's health and diagnose and treat medical conditions. Ultrasound imaging systems are widely used for medical imaging and measurement. The ultrasound transducer probe may include an array of ultrasound transducer elements that transmit acoustic waves into a subject's body and record acoustic waves reflected and backscattered from the internal anatomical structures within the subject's body, which may include tissues, blood vessels, and internal organs. The transmission and reception of acoustic waves, along with various beamforming and processing techniques, create an image of the subject's internal anatomical structures.

Ultrasound imaging is a safe, useful, and in some applications, non-invasive tool for diagnostic examination, interventions, and/or treatment. Ultrasound imaging can be used to diagnose non-alcoholic fatty liver disease (NAFLD), including a stage of progression of NAFLD. Accurately diagnosing an NAFLD stage typically requires advanced quantitative ultrasound (QUS) technology not available in point-of-care (POC) ultrasound systems due to computational or hardware limitations. In addition, acquiring NAFLD images of sufficient quality and analyzing such images typically requires a high level of expertise by the user as well as optimal conditions for the procedure, including sufficient time and resources. A lack of expertise, time, or resources can easily lead to poor quality ultrasound images and inaccurate measurements and diagnoses as a result.

Aspects of the present disclosure are systems, devices, and methods for point-of-care staging of medical conditions through guided ultrasound imaging. Aspects of the present disclosure advantageously guide a non-expert user to obtain ultrasound images of a patient anatomy including multiple views of sufficient quality for analysis. Additionally, a staging value related to the progression of the medical condition is automatically determined based on the acquired ultrasound images. Aspects of the present disclosure also advantageously display to the user image features within the acquired ultrasound images and corresponding sub-scores associated with each image feature. By displaying to the user the images acquired and/or selected, as well as the corresponding sub-scores, the user can be more confident that the resulting staging value is accurate. Aspects of the present disclosure also advantageously reduces time and effort for the physician or user of the system in reporting by automatically generating a report.

The invention is defined by the independent claims. The dependent claims define advantageous embodiments.

In some aspects, an ultrasound imaging system may provide a non-expert user in a point-of-care setting instructions on how to obtain ultrasound images of the patient anatomy from multiple views. The system may analyze the images to ensure that they are of sufficient quality and select at least one image for each view. The ultrasound image may then extract image features corresponding to each view and assign a feature sub-score to each selected image. The ultrasound system may then display the selected images and associated sub-scores as well as determine the staging value for the medical condition based on the images and sub-scores.

According to an exemplary aspect, an ultrasound system is provided. The system includes a processor configured for communication with a display and a transducer array of an ultrasound probe, wherein the processor is configured to: control the transducer array to obtain a first ultrasound image corresponding to a first view of a patient anatomy and a second ultrasound image of a patient anatomy corresponding to a second view of the patient anatomy; identify a first image feature associated with a medical condition of the patient anatomy within the first ultrasound image and a second image feature associated with the medical condition within the second ultrasound image; determine a first sub-score for the first image feature and a second sub-score for the second image feature; determine a staging value representative of a progression of the medical condition based on the first sub-score and the second sub-score; and output, to the display, a screen display comprising: the staging value; and at least one of: the first ultrasound image, an indication of the first image feature in the first ultrasound image, and the first sub-score; or the second ultrasound image, an indication of the second image feature in the second ultrasound image, and the second sub-score.

In some aspects, the processor is configured to output, to the display, user guidance to obtain the first ultrasound image corresponding to the first view of the patient anatomy. In some aspects, the user guidance comprises a graphical representation of a probe and/or orientation for the ultrasound probe. In some aspects, the user guidance comprises a reference image associated with the first view of the patient anatomy. In some aspects, the user guidance comprises a description of a dynamic behavior associated with the first view of the patient anatomy. In some aspects, the processor is configured to determine a quality associated with the first ultrasound image before identifying the first image feature within the first ultrasound image. In some aspects, the processor is configured to determine the quality based on a comparison between the first ultrasound image and a reference image associated with the first view of the patient anatomy. In some aspects, the processor is configured to: if the quality satisfies a threshold, identify the first image feature within the first ultrasound image; if the quality does not satisfy the threshold, control the transducer array to obtain a further ultrasound image corresponding to the first view of the patient anatomy. In some aspects, to determine the first sub-score and the second sub-score, the processor is configured to implement a first machine learning algorithm. In some aspects, the first machine learning algorithm comprises a multi-task learning model. In some aspects, to identify the first image feature and the second image feature, the processor is configured to implement a second machine learning algorithm different than the first machine learning algorithm. In some aspects, the patient anatomy comprises a liver, and the medical condition comprises hepatic steatosis. In some aspects, the first sub-score for the first image feature and the second sub-score for the second image feature correspond to ultrasonographic fatty liver indicator (US-FLI). In some aspects, the first image feature and the second feature each comprise a different one of: liver-kidney contrast, posterior attenuation, vessel blurring, gallbladder visualization, diaphragmatic attenuation visualization, or focal sparing.

According to an exemplary aspect, a computer-implemented method is provided. The method includes controlling a transducer array of an ultrasound imaging probe to obtain a first ultrasound image corresponding to a first view of a patient anatomy and a second ultrasound image of a patient anatomy corresponding to a second view of the patient anatomy; identifying a first image feature associated with a medical condition of the patient anatomy within the first ultrasound image and a second image feature associated with the medical condition within the second ultrasound image; determining a first sub-score for the first image feature and a second sub-score for the second image feature; determining a staging value representative of a progression of the medical condition based on the first sub-score and the second sub-score; and outputting, to the display, a screen display comprising: the staging value; and at least one of: the first ultrasound image, an indication of the first image feature in the first ultrasound image, and the first sub-score; or the second ultrasound image, an indication of the second image feature in the second ultrasound image, and the second sub-score.

1 Claimalso covers an ultrasound system for staging a medical condition, which system includes an ultrasound probe comprising a transducer array; a display; and a processor configured for communication with the display and the transducer array, wherein the processor is configured to: output, to the display, user guidance to obtain a plurality of ultrasound images corresponding to a plurality of views of a patient anatomy; control the transducer array to obtain the plurality of ultrasound images corresponding to the plurality of views; identify, using a first machine learning algorithm, a plurality of image features associated with the medical condition within the plurality of images; determine, using a second machine learning algorithm, a plurality of sub-scores for the plurality of image features according to a clinically-accepted scale; determine a staging value representative of a progression of the medical condition based on the plurality of sub-scores; and output, to the display, a screen display comprising: the staging value; and a visual representation of how the staging value was determined, comprising: at least one ultrasound image of the plurality of ultrasound images; an indication of a corresponding image feature of the plurality of image features in the at least one ultrasound image; a corresponding sub-score of the plurality of sub-scores.

Additional aspects, features, and advantages of the present disclosure will become apparent from the following detailed description.

For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the aspects illustrated in the drawings, and specific language will be used to describe the same. It is nevertheless understood that no limitation to the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, and methods, and any further application of the principles of the present disclosure are fully contemplated and included within the present disclosure as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one aspect may be combined with the features, components, and/or steps described with respect to other aspects of the present disclosure. For the sake of brevity, however, the numerous iterations of these combinations will not be described separately.

1 FIG. 100 100 100 110 130 120 110 112 114 116 118 130 132 134 136 138 is a schematic diagram of an ultrasound imaging system, according to aspects of the present disclosure. The systemis used for scanning an area or volume of a subject's body. A subject may include a patient of an ultrasound imaging procedure, or any other person, or any suitable living or non-living organism or structure. The systemincludes an ultrasound imaging probein communication with a hostover a communication interface or link. The probemay include a transducer array, a beamformer, a processor circuit, and a communication interface. The hostmay include a display, a processor circuit, a communication interface, and a memorystoring subject information.

110 111 112 111 110 112 110 110 110 In some aspects, the probeis an external ultrasound imaging device including a housingconfigured for handheld operation by a user. The transducer arraycan be configured to obtain ultrasound data while the user grasps the housingof the probesuch that the transducer arrayis positioned adjacent to or in contact with a subject's skin. The probeis configured to obtain ultrasound data of anatomy within the subject's body while the probeis positioned outside of the subject's body for general imaging, such as for abdomen imaging, liver imaging, etc. In some aspects, the probecan be an external ultrasound probe, a transthoracic probe, and/or a curved array probe.

110 111 110 110 In other aspects, the probecan be an internal ultrasound imaging device and may comprise a housingconfigured to be positioned within a lumen of a subject's body for general imaging, such as for abdomen imaging, liver imaging, etc. In some aspects, the probemay be a curved array probe. Probemay be of any suitable form for any suitable ultrasound imaging application including both external and internal ultrasound imaging.

In some aspects, aspects of the present disclosure can be implemented with medical images of subjects obtained using any suitable medical imaging device and/or modality. Examples of medical images and medical imaging devices include x-ray images (angiographic images, fluoroscopic images, images with or without contrast) obtained by an x-ray imaging device, computed tomography (CT) images obtained by a CT imaging device, positron emission tomography-computed tomography (PET-CT) images obtained by a PET-CT imaging device, magnetic resonance images (MRI) obtained by an MRI device, single-photon emission computed tomography (SPECT) images obtained by a SPECT imaging device, optical coherence tomography (OCT) images obtained by an OCT imaging device, and intravascular photoacoustic (IVPA) images obtained by an IVPA imaging device. The medical imaging device can obtain the medical images while positioned outside the subject body, spaced from the subject body, adjacent to the subject body, in contact with the subject body, and/or inside the subject body.

112 105 105 112 112 112 112 112 112 112 112 For an ultrasound imaging device, the transducer arrayemits ultrasound signals towards an anatomical objectof a subject and receives echo signals reflected from the objectback to the transducer array. The ultrasound transducer arraycan include any suitable number of acoustic elements, including one or more acoustic elements and/or a plurality of acoustic elements. In some instances, the transducer arrayincludes a single acoustic element. In some instances, the transducer arraymay include an array of acoustic elements with any number of acoustic elements in any suitable configuration. For example, the transducer arraycan include between 1 acoustic element and 10000 acoustic elements, including values such as 2 acoustic elements, 4 acoustic elements, 36 acoustic elements, 64 acoustic elements, 128 acoustic elements, 500 acoustic elements, 812 acoustic elements, 1000 acoustic elements, 3000 acoustic elements, 8000 acoustic elements, and/or other values both larger and smaller. In some instances, the transducer arraymay include an array of acoustic elements with any number of acoustic elements in any suitable configuration, such as a linear array, a planar array, a curved array, a curvilinear array, a circumferential array, an annular array, a phased array, a matrix array, a one-dimensional (1D) array, a 1.x dimensional array (e.g., a 1.5D array), or a two-dimensional (2D) array. The array of acoustic elements (e.g., one or more rows, one or more columns, and/or one or more orientations) can be uniformly or independently controlled and activated. The transducer arraycan be configured to obtain one-dimensional, two-dimensional, and/or three-dimensional images of a subject's anatomy. In some aspects, the transducer arraymay include a piezoelectric micromachined ultrasound transducer (PMUT), capacitive micromachined ultrasonic transducer (CMUT), single crystal, lead zirconate titanate (PZT), PZT composite, other suitable transducer types, and/or combinations thereof.

105 105 The objectmay include any anatomy or anatomical feature, such as a kidney, liver, and/or any other anatomy of a subject. The present disclosure can be implemented in the context of any number of anatomical locations and tissue types, including without limitation, organs including the liver, kidneys, gall bladder, pancreas, lungs; ducts; intestines; nervous system structures including the brain, dural sac, spinal cord and peripheral nerves; the urinary tract; as well as valves within the blood vessels, blood, abdominal organs, and/or other systems of the body. In some aspects, the objectmay include malignancies such as tumors, cysts, lesions, hemorrhages, or blood pools within any part of human anatomy. The anatomy may be a blood vessel, as an artery or a vein of a subject's vascular system, including cardiac vasculature, peripheral vasculature, neural vasculature, renal vasculature, and/or any other suitable lumen inside the body. In addition to natural structures, the present disclosure can be implemented in the context of man-made structures such as, but without limitation, heart valves, stents, shunts, filters, implants and other devices.

114 112 114 112 114 112 110 114 116 114 116 112 114 The beamformeris coupled to the transducer array. The beamformercontrols the transducer array, for example, for transmission of the ultrasound signals and reception of the ultrasound echo signals. In some aspects, the beamformermay apply a time-delay to signals sent to individual acoustic transducers within an array in the transducersuch that an acoustic signal is steered in any suitable direction propagating away from the probe. The beamformermay further provide image signals to the processor circuitbased on the response of the received ultrasound echo signals. The beamformermay include multiple stages of beamforming. The beamforming can reduce the number of signal lines for coupling to the processor circuit. In some aspects, the transducer arrayin combination with the beamformermay be referred to as an ultrasound imaging component.

116 114 116 116 114 118 116 116 116 116 116 134 112 105 The processoris coupled to the beamformer. The processormay also be described as a processor circuit, which can include other components in communication with the processor, such as a memory, beamformer, communication interface, and/or other suitable components. The processormay include a central processing unit (CPU), a graphical processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processormay also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The processoris configured to process the beamformed image signals. For example, the processormay perform filtering and/or quadrature demodulation to condition the image signals. The processorand/orcan be configured to control the arrayto obtain ultrasound data associated with the object.

118 116 118 118 120 130 118 The communication interfaceis coupled to the processor. The communication interfacemay include one or more transmitters, one or more receivers, one or more transceivers, and/or circuitry for transmitting and/or receiving communication signals. The communication interfacecan include hardware components and/or software components implementing a particular communication protocol suitable for transporting signals over the communication linkto the host. The communication interfacecan be referred to as a communication device or a communication interface module.

120 120 120 The communication linkmay be any suitable communication link. For example, the communication linkmay be a wired link, such as a universal serial bus (USB) link or an Ethernet link. Alternatively, the communication linkmay be a wireless link, such as an ultra-wideband (UWB) link, an Institute of Electrical and Electronics Engineers (IEEE) 802.11 WiFi link, or a Bluetooth link.

130 136 136 118 130 At the host, the communication interfacemay receive the image signals. The communication interfacemay be substantially similar to the communication interface. The hostmay be any suitable computing and display device, such as a workstation, a personal computer (PC), a laptop, a tablet, or a mobile phone.

134 136 134 134 138 136 134 134 134 134 110 134 134 134 105 130 134 130 114 The processoris coupled to the communication interface. The processormay also be described as a processor circuit, which can include other components in communication with the processor, such as the memory, the communication interface, and/or other suitable components. The processormay be implemented as a combination of software components and hardware components. The processormay include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processormay also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The processorcan be configured to generate image data from the image signals received from the probe. The processorcan apply advanced signal processing and/or image processing techniques to the image signals. In some aspects, the processorcan form a three-dimensional (3D) volume image from the image data. In some aspects, the processorcan perform real-time processing on the image data to provide a streaming video of ultrasound images of the object. In some aspects, the hostincludes a beamformer. For example, the processorcan be part of and/or otherwise in communication with such a beamformer. The beamformer in the in the hostcan be a system beamformer or a main beamformer (providing one or more subsequent stages of beamforming), while the beamformeris a probe beamformer or micro-beamformer (providing one or more initial stages of beamforming).

138 134 138 134 The memoryis coupled to the processor. The memorymay be any suitable storage device, such as a cache memory (e.g., a cache memory of the processor), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, solid state drives, other forms of volatile and non-volatile memory, or a combination of different types of memory.

138 138 130 138 The memorycan be configured to store subject information, measurements, data, or files relating to a subject's medical history, history of procedures performed, anatomical or biological features, characteristics, or medical conditions associated with a subject, computer readable instructions, such as code, software, or other application, as well as any other suitable information or data. The memorymay be located within the host. Subject information may include measurements, data, files, other forms of medical history, such as but not limited to ultrasound images, ultrasound videos, and/or any imaging information relating to the subject's anatomy. The subject information may include parameters related to an imaging procedure such as an anatomical scan window, a probe orientation, and/or the subject position during an imaging procedure. The memorycan also be configured to store information related to the training and implementation of machine learning algorithms (e.g., neural networks) and/or information related to implementing image recognition algorithms for detecting/segmenting anatomy, image quantification algorithms, and/or image acquisition guidance algorithms, including those described herein.

132 134 132 132 105 The displayis coupled to the processor circuit. The displaymay be a monitor or any suitable display. The displayis configured to display the ultrasound images, image videos, and/or any imaging information of the object.

100 130 130 100 100 138 100 100 The systemmay be used to assist a sonographer in performing an ultrasound scan. The scan may be performed in a at a point-of-care setting. In some instances, the hostis a console or movable cart. In some instances, the hostmay be a mobile device, such as a tablet, a mobile phone, or portable computer. During an imaging procedure, the ultrasound system can acquire an ultrasound image of a particular region of interest within a subject's anatomy. The ultrasound systemmay then analyze the ultrasound image to identify various parameters associated with the acquisition of the image such as the scan window, the probe orientation, the subject position, and/or other parameters. The systemmay then store the image and these associated parameters in the memory. At a subsequent imaging procedure, the systemmay retrieve the previously acquired ultrasound image and associated parameters for display to a user which may be used to guide the user of the systemto use the same or similar parameters in the subsequent imaging procedure, as will be described in more detail hereafter.

134 134 132 In some aspects, the processormay utilize deep learning-based prediction networks to identify parameters of an ultrasound image, including an anatomical scan window, probe orientation, subject position, and/or other parameters. In some aspects, the processormay receive metrics or perform various calculations relating to the region of interest imaged or the subject's physiological state during an imaging procedure. These metrics and/or calculations may also be displayed to the sonographer or other user via the display.

2 FIG. 1 FIG. 210 110 130 116 110 210 134 138 210 210 112 114 118 136 132 100 210 260 264 268 is a schematic diagram of a processor circuit, according to aspects of the present disclosure. One or more processor circuits can be configured to carry out the operations described herein. The processor circuitmay be implemented in the probe, the host systemof, or any other suitable location. For example, the processorof the probecan be part of the processor circuit. For example, the processorand/or the memorycan be part of the processor circuit. In an example, the processor circuitmay be in communication with the transducer array, beamformer, communication interface, communication interface, and/or the display, as well as any other suitable component or circuit within ultrasound system. As shown, the processor circuitmay include a processor, a memory, and a communication module. These elements may be in direct or indirect communication with each other, for example via one or more buses.

260 260 260 260 260 264 The processormay include a CPU, a GPU, a DSP, an application-specific integrated circuit (ASIC), a controller, an FPGA, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processormay also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The processormay also include an analysis module as will be discussed in more detail hereafter. The analysis module may implement various machine learning algorithms and may be a hardware or a software implementation. The processormay additionally include a preprocessor in either hardware or software implementation. The processormay execute various instructions, including instructions stored on a non-transitory computer readable medium, such as the memory.

264 260 264 264 266 266 260 260 110 130 266 266 264 1 FIG. The memorymay include a cache memory (e.g., a cache memory of the processor), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, other forms of volatile and non-volatile memory, or a combination of different types of memory. In some instances, the memoryincludes a non-transitory computer-readable medium. The memorymay store instructions. The instructionsmay include instructions that, when executed by the processor, cause the processorto perform the operations described herein with reference to the probeand/or the host(). Instructionsmay also be referred to as code. The terms “instructions” and “code” should be interpreted broadly to include any type of computer-readable statement(s). For example, the terms “instructions” and “code” may refer to one or more programs, routines, sub-routines, functions, procedures, etc. “Instructions” and “code” may include a single computer-readable statement or many computer-readable statements. Instructionsmay include various aspects of a preprocessor, machine learning algorithm, convolutional neural network (CNN) or various other instructions or code. In some aspect, the memorymay be or include a non-transitory computer readable medium.

268 210 110 130 268 268 210 110 130 1 FIG. 1 FIG. The communication modulecan include any electronic circuitry and/or logic circuitry to facilitate direct or indirect communication of data between the processor circuit, the probe, and/or the host. In that regard, the communication modulecan be an input/output (I/O) device. In some instances, the communication modulefacilitates direct or indirect communication between various elements of the processor circuitand/or the probe() and/or the host().

3 FIG. 1 FIG. 1 FIG. 2 FIG. 300 300 300 300 100 300 116 134 260 is a flow diagram of a methodof obtaining and analyzing ultrasound images corresponding to multiple views of a patient anatomy, according to aspects of the present disclosure. As illustrated, the methodincludes a number of enumerated steps, but aspects of the methodmay include additional steps before, after, or in between the enumerated steps. In some aspects, one or more of the enumerated steps may be omitted, performed in a different order, or performed concurrently. The steps of the methodcan be carried out by any suitable component within the systemand all steps need not be carried out by the same component. In some aspects, one or more steps of the methodcan be performed by, or at the direction of, a processor circuit, including, e.g., the processor(), the processor(), the processor() or any other suitable component.

300 Aspects of the methodmay describe methods of providing guidance to non-expert ultrasound imaging system users to acquire high quality ultrasound images. As will be explained hereafter, these ultrasound images may include multiple views of a region of a patient's anatomy. In addition, aspects of the present disclosure relate to extracting features within the acquired ultrasound images. This extraction may include indicating regions of interest within each ultrasound image. The features extracted may be referred to as image features and may be signs, indicators, or symptoms of how a medical condition manifests itself in ultrasound images. These image features may reflect the severity and/or progression of a particular medical condition. In some aspects, hepatic steatosis (HS) may be a medical condition identified by the ultrasound imaging system. HS may include alcoholic fatty liver disease, as well as non-alcoholic fatty liver disease (NAFLD). In some aspects, NAFLD may correspond to obesity and lead to various liver diseases. In this way, diagnosing NAFLD, other forms of HS, or any other condition, may be accomplished by a non-expert ultrasound user in a point-of-care setting thus expanding the availability of technology capable of diagnosing these conditions. By way of example, a stage of progression may be determined for other medical conditions including diffuse-type disease, such as in the liver and/or other organs, and/or other diseases in other organs.

305 300 210 4 FIG. At step, the methodincludes providing guidance to a user to obtain ultrasound images of a view of a patient liver. Guidance to the user to obtain ultrasound images of a view of the patient liver may be of any form. For example, a processor circuit of the ultrasound system may be in communication with a display. The processor circuit (e.g., the processor circuit) may output to the display a graphical user interface including user guidance. In some aspects, as will be described with reference to, the user guidance may include pictorial graphics of an exemplary patient, as well as an ultrasound system probe and field of view. In some aspects, the user guidance may include text describing the position of the ultrasound probe in relation to the patient's body. In some aspects, the graphical user interface displayed to the non-expert user may include graphics and/or text which may inform the non-expert user where to place the probe, how to move the probe, including in which direction to move the probe as well as various rotational positions, or any other way.

In some aspects, guidance displayed to the user may include highlighting or outlining the features within acquired images in real time. For example, a live imaging loop may be displayed to the non-expert user as the user positions the ultrasound imaging probe adjacent to the patient's body. As live images are displayed, outlines of various features within the live images may be displayed to assist the user in positioning the ultrasound imaging probe in the correct location.

In some aspects, user guidance may include the display of a reference image. For example, the user of the ultrasound system may acquire ultrasound images in real time and compare the acquired ultrasound images to the reference image to determine whether the probe is positioned in the correct position relative to the patient's body.

In some aspects, user guidance may additionally include an indication of the expected dynamic behavior of the patient anatomy during live imaging. For example, movement of particular structures including the liver, kidney, diaphragm, etc., or any other features within the patient anatomy may be identified and described. This dynamic behavior may be described with text, as well as with exemplary images including live images or a series of images including a video clip.

4 5 FIGS.- Additional aspects of providing guidance to a non-expert user to acquire high quality ultrasound images of multiple views of the patient anatomy will be described in more detail with reference tohereafter.

310 300 310 310 100 315 300 315 100 310 300 100 1 FIG. 6 8 FIGS.- At step, the methodincludes receiving ultrasound images. The ultrasound images received at the stepmay include any suitable ultrasound images. For example, in some aspects, the ultrasound images received may include B-mode images. Additionally, any suitable type of ultrasound imaging system may be used to acquire the ultrasound images at step. For example an ultrasound imaging system may be similar to the ultrasound imaging systemdescribed with reference to. At step, the methodincludes performing a quality check of the ultrasound images. At step, the ultrasound imaging systemmay ensure that the images received at the stepare of sufficient quality to perform the subsequent steps of the method. Performing a quality check of the received ultrasound images may include comparing the ultrasound images to corresponding reference images, as well as determining whether features of interest are present within ultrasound images, including whether the entire feature is present or a portion of the feature is present. In some aspects, the quality check of the received ultrasound images may be performed by a user of the ultrasound imaging system, or a machine learning network of the ultrasound imaging system. For example, a machine learning network may be trained to analyze and/or compare received ultrasound images and assign a score relating to the quality of the ultrasound images. Additional aspects of performing quality checks of obtained ultrasound images will be described with reference tohereafter.

300 300 300 300 305 310 315 305 210 100 310 315 315 210 305 305 310 315 210 310 315 210 305 210 300 3 FIG. It is noted that the methodmay be configured to identify multiple features within multiple ultrasound images received. For example, if the methodis implemented to determine a staging score for NAFLD, the methodmay assist the user in obtaining multiple images corresponding to multiple views of the liver kidney region of the patient anatomy. In some aspects, six features of the liver-kidney region of the patient anatomy may indicate the degree of NAFLD within a patient. The methodmay be configured to extract these six features within the obtained ultrasound images. As will be explained hereafter, it is contemplated that any number of features may be extracted corresponding to any suitable medical condition. In the example of identifying six features associated with NAFLD, the steps,, andmay be performed multiple times for each of the six features. For example, at the first time the stepis performed, the processor circuitof the ultrasound imaging systemmay provide guidance to the system user to obtain ultrasound images related to a feature of liver-kidney contrast. At the step, these ultrasound images may be obtained, and at the step, these ultrasound images may be analyzed to ensure that they are of sufficient quality. After the performance of stepin relation to the first feature liver-kidney contrast, the processor circuitmay revert to the stepand perform the steps,, andin relation to a second feature, for example, posterior attenuation. The processor circuitmay then perform the stepsandto receive additional ultrasound images relating to this feature of posterior attenuation, and then the processor circuitmay again revert to the steprelating to an additional feature, and so on. In some aspects, the processor circuitmay perform any subset of the steps shown and described with reference to the methodoffor each identified feature before repeating any of those same steps for an additional feature.

320 300 100 210 100 At step, the methodincludes determining whether the ultrasound images are of sufficient quality. Determining whether the ultrasound images are sufficient quality may include any suitable process. For example, a guidance toolkit may be provided to assist the user in checking if the acquired images are of sufficient quality. In some aspects, image quality may be performed visually by comparing current acquired images with reference images. An inexperienced user may be provided with a reference image adjacent to acquired images for comparison. In some examples, an experienced user may verify the image quality of the obtained images without reference to the reference image. In some aspects, the ultrasound imaging systemmay automatically perform an image quality check with various algorithms or modules. For example, the processor circuitof the ultrasound imaging systemmay be configured to implement a machine learning algorithm or image processing techniques to automatically assess the quality of a particular image.

325 300 315 100 305 At step, the methodincludes selecting one or more ultrasound images per view. As described with reference to the steppreviously, multiple features of the target region of the patient anatomy may be necessary to perform an accurate staging of a medical condition of the patient. In this way, different views of the patient anatomy may better illustrate different features. For example, a one-to-one relationship may exist between views and features. For purposes of the present disclosure, a view may correspond to a probe position relative to the patient anatomy. In this way, for each feature to be extracted from acquired ultrasound images, the ultrasound imaging systemmay provide different guidance to the user to position the ultrasound imaging probe at different locations relative to the patient (e.g., at step).

310 100 However, at the step, multiple ultrasound images may be received corresponding to the same view or probe position. As a result, the ultrasound imaging systemmay select one ultrasound image corresponding to one view for each of the views of the patient anatomy. In some aspects, the ultrasound imaging system may be configured to select additional ultrasound images for a single view.

100 6 FIG. Selecting one or more ultrasound images for each view may be accomplished by comparing each of the ultrasound images associated with a reference image. For example, the ultrasound imaging system user may examine each of the ultrasound images associated with a single view and compare them with the reference image. The ultrasound imaging system user may then select the ultrasound image of the multiple ultrasound images associated with the view that corresponds most closely to the reference image. In some aspects, the ultrasound imaging systemmay automatically select the one or more ultrasound images for each view. For example, various image processing techniques, as well as machine learning algorithms, may be implemented to select an image of the multiple ultrasound images received related to a particular view which corresponds most closely to the reference image. Additional aspects of selecting one or more ultrasound images for each view will be described in more detail with reference to.

330 300 At step, the methodincludes determining whether the selected ultrasound image shows cirrhosis. In some implementations, determining whether the selected ultrasound image shows cirrhosis includes analyzing the liver background within the selected image. In some aspects, elastography may be used to determine if the liver background indicates cirrhosis. In implementations in which elastography is unavailable, the liver background of the ultrasound image could be used to identify if the liver background corresponds to a normal or healthy state or if the liver background indicates cirrhosis.

330 325 325 210 100 330 330 310 In some aspects, the stepmay be performed by analyzing any one of the ultrasound images selected at the step. For example, in an implementation in which six features are selected and six ultrasound images are selected at the step, the processor circuitof the ultrasound imaging systemmay analyze each of these six ultrasound images at the stepto determine whether the liver background indicates cirrhosis. In some aspects, only one of these six ultrasound images may be selected. In still other aspects, a separate ultrasound image may be selected and analyzed at the step, including any of the ultrasound images received at the step.

330 100 800 310 325 8 FIG. It is noted that any of the machine learning algorithms described herein may be configured to perform the step. For example, the ultrasound imaging system, as well as any machine learning algorithm, such as the algorithmdescribed with reference to, may include a cirrhosis analysis module. This cirrhosis analysis module may be configured to determine whether the ultrasound images received at the stepand or selected at the stepindicates cirrhosis.

335 300 310 300 335 335 100 300 335 At step, the methodincludes outputting an indication of cirrhosis. In some examples, assigning a staging value of NAFLD, or any other medical condition, may not be sufficiently accurate if the ultrasound images received at the stepindicates cirrhosis. In such an example, the methodproceeds to the step. The stepincludes indicating to the user that correct liver staging may not be provided in this situation with the ultrasound imaging systemand associated method. In this way, the methodmay end at the step.

335 100 300 In some aspects, after the stepis completed, the ultrasound imaging systemmay further generate a report for the user of the ultrasound imaging system, the patient, or another physician, indicating that the ultrasound images indicated cirrhosis and that the methodwas not completed in its entirety.

340 300 300 340 330 310 305 315 340 350 300 210 210 340 345 210 340 350 340 350 At step, the methodincludes extracting a feature from the selected ultrasound image. The methodmay proceed to the stepif, at the step, it is determined that the ultrasound images obtained at the stepdo not indicate cirrhosis. As described previously with reference to steps-, the steps-may be performed multiple times for each identified feature. In an example in which the methodis implemented to diagnose a staging value of NAFLD and the processor circuitis configured to identify and extract 6 features, the processor circuitmay identify the ultrasound image corresponding to the first feature (e.g., liver-kidney contrast) and extract this feature from the selected ultrasound image at the step. After then completing the steps(described hereafter), the processor circuitmay identify the ultrasound image corresponding to the second feature (e.g., posterior attenuation) and perform the steps-again. This process may continue for each feature or view until all the selected ultrasound images corresponding to the different views have been analyzed according to the steps-.

210 210 210 210 210 210 8 FIG. The processor circuitmay extract features from ultrasound images in any suitable way. For example, as will be explained in more detail hereafter with reference to, the processor circuitmay implement a machine learning algorithm to extract features from the selected ultrasound images. In some instances, the processor circuitmay apply various pre-processing techniques to a selected ultrasound image prior to extracting features. For example, the processor circuitmay adjust various characteristics of the selected image, such as, for example, an image contrast, brightness, image size, coloration, or any other characteristic. In some instances, the processor circuitmay additionally apply various image processing techniques to the selected ultrasound image prior to or after implementation of the machine learning network. For example, the processor circuitmay perform various image processing techniques, such as edge identification of features within the selected ultrasound images, pixel-by-pixel analysis to determine transition between light pixels and dark pixels, filtering, or any other suitable techniques to determine the location of various organs or structures shown within the selected ultrasound images.

345 300 210 340 At step, the methodincludes assigning a feature score. In some aspects, the processor circuitmay assign a feature score to the selected ultrasound image by implementing a machine learning algorithm. The machine learning algorithm which assigns a score to the selected ultrasound image after the corresponding feature has been extracted may be the same machine learning algorithm which may be used to extract the features at the step. In some aspects, the machine learning algorithm for assigning a features score may be a separate algorithm.

210 210 210 The scoring convention implemented by the processor circuitmay be of any suitable type. For example, the processor circuitmay be configured to assign scores to features and/or ultrasound images using the ultrasonographic fatty liver indicator (US-FLI) scoring system. For example, the features which the processor circuitis configured to extract may include liver-kidney contrast, posterior attenuation, portal vein blurring, gallbladder wall visualization, diaphragm definition, and focal fat sparing. As noted, any suitable number or types of features may be extracted from the ultrasound images selected, including any features related to different medical conditions. In this example, the liver-kidney contrast features may be assigned an integer score on the scale of 0-3, where a score of 0 corresponds to an isoechoic region, and a score of 3 corresponds to a severely hyperechoic region. The remaining features, including the posterior attenuation, portal vein blurring, gallbladder wall visualization, diaphragm definition, and focal fat sparing, may be assigned an integer score of 0 or 1. For example, posterior attenuation may be assigned a score of 1 if the ultrasound image shows attenuation, and 0 if the ultrasound image shows no attenuation. The portal vein blurring feature may be assigned a score of 1 if vessel blurring is present within the selected ultrasound image and 0 if vessel blurring is not present. The gallbladder wall visualization score may be a 1 if it is difficult to visualize the gallbladder wall and 0 if the gallbladder wall is easily visualized. The diaphragm definition feature may be assigned a score of 1 if the diaphragm is obscured in the corresponding ultrasound image and 0 if the diaphragm is clearly defined. The focal fat sparing feature may be assigned a score of 1 if focal fat sparing is present in the corresponding ultrasound image and 0 if focal fat sparing is not present.

For any of the scoring conventions described above relating to the six features of NAFLD, the score ranges could be different, including any suitable range. In addition, assigned scores could be non-integer numbers. Other scoring conventions may include scoring features with percentages, measurements or values of features within the selected ultrasound image, such as an estimated volume or weight of fat or other tissue or material, as well as any other scoring conventions.

350 300 345 210 350 310 325 340 345 350 210 132 325 345 300 300 350 9 9 FIGS.A-F At step, the methodincludes displaying the selected ultrasound image, an indication of the extracted features, and/or the feature score. After each feature is assigned a score at the step, the processor circuitmay display the results of the score assignment at the step. For example, with regard to a kidney-liver contrast feature, multiple images may have been acquired at the steprelating to the kidney-liver contrast. At the step, one of these ultrasound images may have been selected as the ultrasound image which most strongly correlates to the liver-kidney contrast features. At the step, the processor circuit may extract or identify the liver-kidney contrast feature from the selected image. At the step, a score may be assigned to the feature. At the step, the processor circuitmay output to the display (e.g., the display) the ultrasound image selected at the stepalong with an indication that this ultrasound image was selected to show the liver-kidney contrast feature, and the score assigned to the image and/or feature at the step. In this way, the user may confirm that the ultrasound image was correctly selected and analyzed. In some aspects, this process of displaying to the user the selected ultrasound image and corresponding feature and feature score may help a non-expert user of the ultrasound image to trust the results of the method, thus increasing the likelihood that the methodwill be more widely adopted. Additional details of the stepwill be described in greater detail with reference tohereafter.

355 300 8 FIG. At step, the methodincludes determining a staging score or staging value. As will be described with reference to, a machine learning algorithm may be implemented to determine the staging score. In some aspects, the machine learning algorithm which may determine the stage score may the same machine learning algorithm which may be used to extract features from selected ultrasound images and/or the same machine learning algorithm which may be used to assign feature scores to extracted features. The staging value may include a numerical value (e.g., an integer or value within a range) or text (e.g., normal, mild, moderate, or severe).

210 In some aspects, various mathematical formulae or relationships may be implemented to determine the staging score based on the individual feature scores previously described. In one example, the staging score may be a sum of each of the individual feature scores. For example, the individual feature scores may be added resulting in the staging score. Various ranges of the staging score may determine the stage of NAFLD of the patient. In one example, a staging score of 0-1 may correspond to a normal liver showing no signs of NAFLD. A staging score of 2-3 may indicate mild NAFLD or stage 1 NAFLD. A staging score of 4-5 may indicate moderate or stage 2 NAFLD and a staging score of 6 or more may indicate severe or stage 3 NAFLD. Other relationships may also be established, including the staging score being an average, median, or weighted average of the individual feature scores. In addition, any suitable linear or non-linear relationship may be established between the various features scores and the final staging score. For example, the processor circuitmay weight different individual feature scores differently than others depending on the level of correlation between any one feature and the NAFLD stage.

360 300 355 210 At step, the methodincludes displaying the staging score, one or more ultrasound images, one or more indications of extracted features, and/or one or more feature scores. After the staging score is determined at the step, the processor circuitmay display the information.

360 210 210 300 10 11 FIGS.- Additional aspects of stepwill be explained in more detail with reference to. The processor circuitmay be configured to display each of the selected ultrasound images corresponding to each feature as well as the corresponding feature scores of each features. The processor circuitmay additionally show the staging score, including the stage (e.g., stage 1, 2, or 3) of NAFLD of the patient. In some aspects, displaying this information may increase the level of trust the user, particular a non-expert user, may have in the results of the method.

210 300 300 300 In some aspects, the processor circuitmay additionally generate a report including the results of the methodfor the user, the patient, the physician, another specialist physician, or any other individual. This report may include a document including the ultrasound images selected, the features, feature scores, the final staging score, and the stage. In some aspects, the report generated may also describe the method of determining the final staging score based on the individual feature scores as well as the method of extracting features and assigning feature scores. In some implementations, an expert physician may review this report to ensure that the methodwas successfully completed and the staging diagnosis was performed accurately. Additional features and details of the methodwill be shown and described with reference to the following figures.

4 FIG. 400 305 300 is a diagrammatic view of a graphical user interfacedisplaying user guidance for obtaining ultrasound images for diagnosis, according to aspects of the present disclosure. As described with reference to stepof the method, user-guidance may include graphical representations of a patient's body with a probe positioned next to the body, text describing the position of the patient and probe, a reference image providing an example of the type of image the user needs to acquire, and a description of expected dynamic movement of the patient anatomy within the expected ultrasound images.

480 480 400 400 4 FIG. The graphical user interface may include a title. The titlemay indicate to the user of the ultrasound image the type of ultrasound image to be obtained, including the view of the patient anatomy to be obtained, as well as the feature to be extracted from the resulting images. In the example shown in, the graphical user interfacemay indicate to the user that the user guidance provided within the interfacecorrespond to obtaining an ultrasound image of a first feature (e.g., liver-kidney contrast).

400 410 410 412 420 The user guidance within the graphical user interfaceincludes a graphical representation. The graphical representationmay include a stylized graphic of a patient body, and a stylized graphic of an ultrasound transducer probe.

412 420 412 414 405 405 405 425 420 425 420 4 FIG. 4 FIG. The patient bodymay graphically describe the position of the patient for obtaining ultrasound images of the view, in this case, view A. In the example provided, the user guidance may show the patient in the supine position with the right arm maximally abducted. The probemay be shown adjacent to the patient bodyat the locationrelative to the patient anatomy and may illustrate the desired orientation of the probe. In addition, corresponding textmay indicate the probe position to the user as well. For example, the textshown in the example ofmay indicate that the probe position should be a right intercostal position. In some examples, the textmay additionally indicate a plane of view of the ultrasound imaging probe. The plane of view may include a transverse plane, a sagittal plane, or a frontal plane. As shown in, a field of viewmay be displayed extending from the distal end of the probe. This field of viewmay assist a physician in directing the probeinto the patient anatomy.

In some examples, to obtain the view A corresponding to the liver-kidney contrast features and/or any other view or feature described herein, the probe may be positioned such that the hepatic-renal interface is positioned in the middle region of the B-mode ultrasound image. This may help to avoid ultrasound propagation distortion through the inter-organ (e.g., the liver or kidney) boundaries. In some aspects, additional user guidance may include indicating that the patient should hold their breath during imaging, or any other instructions for the patient, physician, or user. User guidance may include various imaging control parameters, such as intensity, gain, focus, beam steering, time-gain compensation, or any other parameters.

455 400 455 455 455 455 450 The reference imagemay additionally be included in the graphical user interface. In some aspects, the reference imagemay be an image of the same region of anatomy from a different patient. The reference imagemay be an image acquired by an expert user of the ultrasound imaging system. The reference image may be an image showing the view A (e.g., liver-kidney contrast) of the region. The reference imagemay be a view of a liver-kidney region at any suitable stage of NAFLD. In some aspects, the reference imagemay be associated with a label.

4 FIG. 400 460 455 455 As also shown in, the graphical user interfaceincludes an indication of expected dynamic behavior of the ultrasound images acquired at the desired view. In some aspects, the expected dynamic behavior may include textdescribing the expected dynamic behavior. For example, the expected dynamic behavior for a particular view may include the diaphragm moving below the liver. In some aspects, any other dynamic behavior may be expected. In some aspects, the expected dynamic behavior may include a loop of multiple ultrasound images showing movement within the patient anatomy. In some aspects, this loop may replace the reference image. The reference imagemay include multiple images obtained in succession at a previous imaging procedure by an expert sonographer. In some aspects, the expected dynamic behavior may include an animated image or stylized loop of images showing the expected movement.

100 400 500 500 100 500 5 FIG. 5 FIG. As previously mentioned, showing the user of the ultrasound imaging systemeach of these aspects of the graphical user interfacemay help the user to feel confident that the imaging procedure is being performed correctly and that the final result (e.g., a NAFLD staging value) is accurate.is a diagrammatic view of a tableof user guidance for obtaining ultrasound images of different views for diagnosis, according to aspects of the present disclosure. As shown in, the tablemay provide a user of the systemwith a checklist including attributes of the acquired ultrasound images that should be met. It is noted that the tablemay be organized in any suitable way, including additional rows or columns or having fewer rows or columns as those shown.

502 500 502 522 524 526 A first columnof the tableincludes a list of the views to be obtained. In some aspects, the views may include six views corresponding to a liver-kidney contrast, posterior attenuation, portal vein blurring, gallbladder wall visualization, diaphragm definition, and focal fat sparing. It is understood, however, that any number or type of views may be included in the column. Each of these views may define a row (e.g., rows,, and, etc.) Each subsequent column may provide the user with an attribute or information related to each of these views.

504 522 504 100 504 506 500 The columnmay include an acquired image corresponding to the view. With reference to view A in row, the acquired image of columnmay be an image acquired by the user of the ultrasound imaging systemduring the present procedure. The acquired imagemay be compared to the reference imageand/or analyzed according to any of the other criteria listed in the table.

506 455 504 506 504 522 4 FIG. In some examples, the reference image of columnmay be the same image as the imageofor may differ depending on the view. The acquired image of columnmay be compared to the reference image of the columnto ensure that the acquired imageaccurately captures the view of the rowand other rows.

508 410 4 FIG. The columnmay include information or directions related to the probe location and/or angle. This information may be of any suitable form including text or visual graphics, images, or symbols. For example, the probe location and/or angle may be substantially similar to the graphical representationshown and described with reference to.

510 510 460 4 FIG. The columnmay include the expected dynamic behavior of the acquired image or images. The expected dynamic behavior may be shown in any suitable form. For example, the expected dynamic behavior listed in the columnmay be substantially similar to the expected dynamic behaviordescribed with reference to.

512 100 The columnmay include an order in which the various views are to be acquired. In some aspects, the order may be determined by the user of the ultrasound imaging systemprior to the imaging procedure. In some aspects, the order may be provided to the user and may be based on a recommended order. In some aspects, the order of views may not be specified.

500 The tablemay be at least a portion of a guidance toolkit provided to the user to help the user to check if the acquired images or sequence is of sufficient quality with clear appearances of the kidney and/or liver, or other structures to ensure that the final staging value determined is accurate.

6 FIG. 100 is a diagrammatic view of acquired ultrasound images of multiple views and corresponding reference images for comparison, according to aspects of the present disclosure. In some aspects, the multiple ultrasound images may be acquired corresponding to a single view. For example, during the ultrasound imaging procedure, the user of the systemmay specify the intended view to be captured prior to acquiring ultrasound images. Based on this indication, each ultrasound image acquired after the indication is received may be associated with the particular view.

100 620 620 620 630 625 630 455 4 FIG. For example, the user of the ultrasound imaging systemmay provide a user input indicating that the view A is the intended view to be captured. After this indication is received, a series of ultrasound imagesmay be received. In some aspects, one ultrasound image of the imagesmay be selected as most accurately capturing the view A. For the purposes of the present disclosure, the image which most accurately captures the intended view may be referred to as the key image. To select the key image for the view A, each of the imagesassociated with the view A may be compared with a reference image, as shown by the arrow. The reference imagemay be the same image as the reference imageshown and described with reference to.

6 FIG. 2 FIG. 622 620 622 622 100 622 100 210 620 630 620 622 620 As shown in, the imageof the imagesmay be selected as the key image. This imagemay be selected according to a variety of methods. In one aspect, the imagemay be selected by the user of the ultrasound imaging system. In another aspect, the imagemay be automatically selected by a processor circuit of the ultrasound imaging system. For example, the processor circuit() may implement various image processing techniques to determine a similarity score between each of the imagesand the reference image. The imagewith the highest similarity score may be selected as the key image. In some aspects, a machine learning network may be used to determine a similarity score for each of the images.

622 210 622 630 In examples in which the key imageis automatically selected by the processor circuit, the user may be provided with a graphical user interface allowing the user to provide a user input confirming that the key imagebest represents the view A and corresponds to the reference image.

6 FIG. 640 640 650 645 642 A similar procedure may be performed for each of the views to select a key image for each view. For example, as shown in, ultrasound imagesmay correspond to the view B. Each of these imagesmay be compared to the reference imageas shown by the arrow. In the example shown, the imagemay be selected as the key image.

660 660 670 665 642 Ultrasound imagesmay correspond to the view C. Each of these imagesmay be compared to the reference imageas shown by the arrow. In the example shown, the imagemay be selected as the key image.

620 640 660 It is noted, that the ultrasound image (e.g., the images,, and) may be of any suitable format. For example, the received ultrasound images may be of an ultrasound DICOM image format or raw data in a point of care ultrasound system.

7 FIG. 7 FIG. is a diagrammatic view of acquired ultrasound images and identified features, according to aspects of the present disclosure. As shown in, each of the ultrasound images may correspond to one or more features. In some aspects, each of the views may correspond to one feature. However, an ultrasound image selected as a key image of one particular view may include a feature of that view as well as another feature corresponding to a different view.

For example, a relationship between six different views and six corresponding features may be a one-to-one correspondence. In some aspects, an image corresponding to one view may include multiple features corresponding to multiple views.

7 FIG. 6 FIG. 710 710 710 622 710 712 712 712 710 714 716 210 710 includes an ultrasound image. The imagemay be a key image for view A. In this way, the imagemay be the same image as the imagedescribed with reference to. The imagemay include the feature. For example, the featuremay correspond to the liver-kidney contrast. The featuremay be extracted from the imageas shown by the arrow. This feature may be stored as feature Ain a memory in communication with the processor circuitand associated with the image.

7 FIG. 6 FIG. 720 720 720 642 720 722 722 722 720 724 726 210 720 Similarly,includes an ultrasound image. The imagemay be a key image for view B. In this way, the imagemay be the same image as the imagedescribed with reference to. The imagemay include the feature. For example, the featuremay correspond to posterior attenuation. The featuremay be extracted from the imageas shown by the arrow. This feature may be stored as feature Bin a memory in communication with the processor circuitand associated with the image.

7 FIG. 6 FIG. 730 730 730 662 730 732 732 732 730 734 additionally includes an ultrasound image. The imagemay be a key image for view C. In this way, the imagemay be the same image as the imagedescribed with reference to. The imagemay include the feature. For example, the featuremay correspond to portal vein blurring. The featuremay be extracted from the imageas shown by the arrow.

736 210 730 This feature may be stored as feature Cin a memory in communication with the processor circuitand associated with the image.

7 FIG. 730 722 742 210 210 722 740 726 730 210 722 722 720 730 As shown in, the imagemay additionally include a portion of the featureas well as a portion of a feature. The processor circuitmay extract all relevant features from any of the images shown. In the example of view C, shown, the processor circuitmay extract the featureas shown by the arrow. In some aspects, a relationship between the features Band the imagemay be established. The processor circuitmay assign a score to the feature, as will be discussed in more detail hereafter, based on the analysis of the featuresas shown in the imageas well as the image.

742 742 210 742 730 744 746 210 730 742 The featuremay be a feature corresponding to a different view, for example, view D. This featuremay be associated with a gallbladder wall visualization. The processor circuitmay extract the featuresfrom the imageas shown by the arrow. The features may be stored as feature Din a memory in communication with the processor circuitand associated with the image. In some aspects, an additional image may correspond to the view D and may also include a depiction of the feature.

8 FIG. 7 FIG. 800 800 100 800 800 is a schematic diagram of a machine learning algorithm, according to aspects of the present disclosure. The machine learning algorithmmay perform various tasks associated with the ultrasound imaging system. For example, the machine learning algorithmmay extract features from ultrasound images, as described with reference to, assign scores to each extracted feature, and assign a staging value based on the feature scores. In some aspects, the machine learning algorithmmay perform other functions such as selecting key images from a group of ultrasound images associated with a particular view, performing quality checks on received ultrasound images, or any other functions described herein.

800 800 800 800 The machine learning algorithmmay include any suitable type of machine learning algorithm. For example, the machine learning algorithmmay include one or more convolutional neural networks (CNNs). Aspects of the machine learning algorithmmay be scaled to include any suitable number of CNNs. The machine learning algorithmcan be trained for identification of various anatomical landmarks or features within a patient anatomy, including a liver-kidney contrast, posterior attenuation, portal vein blurring, gallbladder wall visualization, diaphragm definition, and focal fat sparing, as well as any other features.

805 800 800 At a stepof the algorithm, the machine learning algorithmmay receive ultrasound images. The ultrasound images may be key images. In some aspects, the networkmay receive six key images, each corresponding to at least one view.

810 805 815 At step, the preprocessing of the data (e.g., the images received at step) may be performed. In some aspects, preprocessing may include adjusting parameters of the input images. For example, various filters may be applied to enhance the visibility of image features. For example, filters applied to the images may increase visibility of the boundary of the liver, vessels within the liver, the diaphragm, focal fat sparing if present, or any other features. Preprocessing may also include contrast enhancement, noise reduction, or any other image alteration. In some aspects, the input images may be resized or otherwise modified prior to extracting image features at the step.

815 800 805 805 805 820 805 7 FIG. At step, the machine learning algorithmmay extract features, or regions of interest, from each of the images received at step. In some aspects, this extraction may include identifying regions within each image which most closely correspond to the view of each received image. As explained with reference to, any of the images of stepmay be primarily associated with a single view of the patient anatomy. However, any of the images of stepmay additionally include features corresponding to other views. As shown at the step, the primary feature may be extracted from each image of step. In other aspects, additional features may be extracted from each image and correlated with each other depending on which view each feature corresponds to.

830 800 805 820 855 Stepof the machine learning algorithmmay correspond to a multitask classification step and may include several layers. These layers may be any suitable number of convolutional layers, fully connected layers, or layers of any other type. In some aspects, each convolutional layer may include a set of filters configured to extract features from the input (e.g., the images of stepand/or the features of step). The number of layers and filters implemented may vary depending on the embodiments. In some instances, the convolutional layers and the fully connected layers may utilize a leaky rectified non-linear (ReLU) activation function and/or batch normalization. The fully connected layers may be non-linear and may gradually shrink the high-dimensional output to a dimension of the prediction result (e.g., the classification output or layer). Thus, the fully connected layers may also be referred to as a classifier. In some embodiments, the fully convolutional layers may additionally be referred to as perception or perceptive layers.

830 840 845 850 855 855 840 855 860 862 864 866 868 870 860 862 864 866 868 870 345 300 800 800 860 862 864 866 868 870 800 805 860 862 864 866 868 870 8 FIG. 3 FIG. 7 FIG. In some aspects, the multitask classification stepmay include an input layer, a shared hidden layer, a task-specific hidden layer, and an output layer. The output layermay include six classifications corresponding to each of the six features input at the input layer. In the example shown in, the output layermay determine a scorecorresponding to feature A (e.g., kidney-liver contrast), a scorecorresponding to feature B (e.g., posterior attenuation), a scorecorresponding to feature B (e.g., portal vein blurring), a scorecorresponding to feature D (e.g., gallbladder wall visualization), a scorecorresponding to feature E (e.g., diaphragm definition), and a scorecorresponding to feature F (e.g., focal fat sparing). These scores,,,,, andmay be of a format as those described with reference to stepof the methodof. However, any suitable format may be used, including any suitable ranges, percentages, ratios, or other values. In some aspect, the machine learning algorithmmay additionally determine a confidence level associated with the each feature. This may include the level of confidence with which the networkdetermines that the particular feature is shown within the corresponding image. For the purposes of the present disclosure, the scores,,,,, andmay be referred to as sub-scores. In this way, the sub-scores output from the machine learning algorithmmay be a grading of the extent to which a particular medical condition (e.g., NAFLD) is manifesting in the received ultrasound images. In some aspects, as described with reference to, input images of stepmay include features related to multiple views. As a result, in some aspects, any of the sub-scores,,,,, andmay be based on aspects of not just one input image, but any of the input images which contains information related to the feature of a particular sub-score.

8 FIG. 3 FIG. 880 880 880 355 300 As shown in, a final step of the deep learning algorithm may include determining a staging value. The staging valuemay be the stage of progression of a particular illness or condition being monitored. For example, a stage of 0, 1, 2, or 3 may correspond to an NAFLD condition. The staging valuemay be of any suitable type and calculated in any suitable way, including those described with reference to stepof the method().

800 In some aspects, The machine learning algorithmmay output a feature vector at the output of the last convolutional layer. The feature vector may indicate objects or features detected from a corresponding input image or other data.

800 800 The machine learning algorithmmay implement or include any suitable type of learning network. For example, the machine learning algorithmcould include a convolutional neural network, a multi-class classification network, an encoder-decoder type network, or any suitable network or means of identifying features within an image.

800 805 In an embodiment in which the machine learning algorithmincludes an encoder-decoder network, the network may include two paths. One path may be a contracting path, in which a large image, such as an input image of the step, may be convolved by several convolutional layers such that the size of the image changes in depth of the network. The image may then be represented in a low dimensional space, or a flattened space. From this flattened space, an additional path may expand the flattened space to the original size of the image. In some embodiments, the encoder-decoder network implemented may also be referred to as a principal component analysis (PCA) method. In some embodiments, the encoder-decoder network may segment the image into patches.

800 805 855 In an additional embodiment of the present disclosure, the machine learning algorithmmay include a multi-class classification network. In such an embodiment, the multi-class classification network may include an encoder path. For example, an input image (e.g., one of the images of step) may be a high dimensional image. The image may then be processed with the convolutional layers such that the size is reduced. The resulting low dimensional representation of the image may be used to generate a feature vector. The low dimensional representation of the image may additionally be used by the fully connected layers to regress and output one or more classes, such as the features listed in the output layer. In some regards, the fully connected layers may process the output of the encoder or convolutional layers. The fully connected layers may additionally be referred to as task layers or regression layers, among other terms.

It is noted that in some aspects, more than six features may be extracted and more than six corresponding images may be received illustrating these features. For example, various types of vein blurring may be included as identified features, including large hepatic vein blurring, main right portal vein blurring, or anterior posterior right portal vein blurring. Additional features may include liver echotexture and overall impression.

800 880 800 880 In some aspects, the machine learning algorithmmy include two separated modules for different tasks. For example, one module may be implemented to extract features from input images. Another module may be implemented to determine the staging value. In some aspects, the module for extracting features from input images may be an object detection algorithm, for example Yolov4. In some aspects, the machine learning algorithmmay include two separate machine learning algorithms. For example, one machine learning algorithm may be implemented to extract features from input images. Another machine learning algorithm may be implemented to determine sub-scores associated with the extracted features as well as the staging value.

800 Any suitable combination or variations of the machine learning algorithmdescribed is fully contemplated. For example, the machine learning algorithm may include fully convolutional networks or layers or fully connected networks or layers or a combination of the two. In addition, the machine learning algorithm may include a multi-class classification network, an encoder-decoder network, or a combination of the two.

9 FIG.A 900 902 908 904 909 is a diagrammatic view of a graphical user interfacedisplaying a selected ultrasound imagefor a particular view, a feature, and a score, according to aspects of the present disclosure.

902 900 902 908 904 902 210 902 904 906 907 9 FIG.A The ultrasound imageincluded in the graphical user interfacemay be the key image selected corresponding to the feature of view A. In some aspects, the imagemay be a different image. In the example shown in, the feature identified may be the liver-kidney contrast, as shown by the label. To identify the featurewithin the ultrasound image, the processor circuitmay be configured to overlay a graphical element corresponding to the feature on the ultrasound image. In some aspects, the featuremay include a region of the liverand a region of the kidney.

900 908 909 909 860 855 800 909 900 909 909 9 FIG.A The graphical user interfacemay include a label identifying the view Aand the corresponding feature (e.g., liver-kidney contrast). An additional label provides the scoreassigned to the particular feature. The scoremay be the scoredescribed as a result of the output layerof the machine learning algorithm. In some aspects, the scoremay be accompanied by a description of the score. In the graphical user interfaceprovided in, for example, the scoreof three may be accompanied by the description, “severely hyperechoic.” Varying descriptions may be included corresponding to other values of the score.

900 904 904 902 In some aspects, the graphical user interfacemay additionally include a region configured to receive a user input, such as a button, text box, or any other form of user input. This user input may allow the user to confirm that the featurecorrectly identifies the corresponding view A and feature (e.g., liver-kidney contrast). In some aspects, the user may adjust the location of the featurewithin the image, or provide other user inputs.

9 FIG.B 910 912 916 914 918 is a diagrammatic view of a graphical user interfacedisplaying a selected ultrasound imagefor a particular view, a feature, and a score, according to aspects of the present disclosure.

912 910 916 210 912 914 9 FIG.B The ultrasound imageincluded in the graphical user interfacemay be the key image selected corresponding to the feature of view or may be a different image. In the example shown in, the feature identified may be posterior attenuation, as shown by the label. The processor circuitmay overlay a graphical element over the imageidentifying the feature.

910 916 918 918 862 855 800 918 918 The graphical user interfacemay include a label identifying the view Band the corresponding feature (e.g., posterior attenuation). An additional label provides the scoreassigned to the particular feature. The scoremay be the scoredescribed as a result of the output layerof the machine learning algorithm. In some aspects, the scoremay be accompanied by a description of the score, such as, “attenuation.” Varying descriptions may be included corresponding to other values of the score.

900 910 914 9 FIG.A 9 FIG.B Like the graphical user interfaceof, the graphical user interfaceofmay additionally include a region configured to receive a user input confirming that the featurecorrectly identifies the corresponding view B and feature.

9 FIG.C 920 922 926 924 928 is a diagrammatic view of a graphical user interfacedisplaying a selected ultrasound imagefor a particular view, a feature, and a score, according to aspects of the present disclosure.

922 920 926 210 922 924 9 FIG.C The ultrasound imageincluded in the graphical user interfacemay be the key image selected corresponding to the feature of view or may be a different image. In the example shown in, the feature identified may be portal vein blurring, as shown by the label. The processor circuitmay overlay a graphical element over the imageidentifying the feature.

920 926 928 928 864 855 800 928 928 920 924 9 FIG.C The graphical user interfacemay include a label identifying the view Cand the corresponding feature (e.g., portal vein blurring). An additional label provides the scoreassigned to the particular feature. The scoremay be the scoredescribed as a result of the output layerof the machine learning algorithm. In some aspects, the scoremay be accompanied by a description of the score, such as, “blurring” or varying descriptions corresponding to other values of the score. Like the graphical user interfaces previously described, the graphical user interfaceofmay include a region configured to receive a user input confirming that the featurecorrectly identifies the corresponding view C and feature.

9 FIG.D 930 932 936 934 938 is a diagrammatic view of a graphical user interfacedisplaying a selected ultrasound imagefor a particular view, a feature, and a score, according to aspects of the present disclosure.

932 930 936 210 932 934 9 FIG.D The ultrasound imageincluded in the graphical user interfacemay be the key image selected corresponding to the feature of view or may be a different image. In the example shown in, the feature identified may be gallbladder wall visualization, as shown by the label. The processor circuitmay overlay a graphical element over the imageidentifying the feature.

930 936 938 938 866 855 800 938 938 The graphical user interfacemay include a label identifying the view Dand the corresponding feature (e.g., gallbladder wall visualization). An additional label provides the scoreassigned to the particular feature. The scoremay be the scoredescribed as a result of the output layerof the machine learning algorithm. In some aspects, the scoremay be accompanied by a description of the score, such as, “poor visualization” or varying descriptions corresponding to other values of the score.

930 934 9 FIG.D Like the graphical user interfaces previously described, the graphical user interfaceofmay include a region configured to receive a user input confirming that the featurecorrectly identifies the corresponding view D and feature.

9 FIG.E 940 942 946 944 948 is a diagrammatic view of a graphical user interfacedisplaying a selected ultrasound imagefor a particular view, a feature, and a score, according to aspects of the present disclosure.

942 940 946 210 942 944 9 FIG.E The ultrasound imageincluded in the graphical user interfacemay be the key image selected corresponding to the feature of view or may be a different image. In the example shown in, the feature identified may be diaphragm definition, as shown by the label. The processor circuitmay overlay a graphical element over the imageidentifying the feature.

940 946 948 948 868 855 800 948 948 The graphical user interfacemay include a label identifying the view Eand the corresponding feature (e.g., gallbladder wall visualization). An additional label provides the scoreassigned to the particular feature. The scoremay be the scoredescribed as a result of the output layerof the machine learning algorithm. In some aspects, the scoremay be accompanied by a description of the score, such as, “obscured” or varying descriptions corresponding to other values of the score.

940 944 9 FIG.E Like the graphical user interfaces previously described, the graphical user interfaceofmay include a region configured to receive a user input confirming that the featurecorrectly identifies the corresponding view E and feature.

9 FIG.F 950 952 956 954 958 is a diagrammatic view of a graphical user interfacedisplaying a selected ultrasound imagefor a particular view, a feature, and a score, according to aspects of the present disclosure.

952 950 956 210 952 954 9 FIG.F The ultrasound imageincluded in the graphical user interfacemay be the key image selected corresponding to the feature of view or may be a different image. In the example shown in, the feature identified may be focal fat sparing, as shown by the label. The processor circuitmay overlay a graphical element over the imageidentifying the feature.

950 956 958 958 870 855 800 958 958 950 954 9 FIG.F The graphical user interfacemay include a label identifying the view Fand the corresponding feature (e.g., focal fat sparing). An additional label provides the scoreassigned to the particular feature. The scoremay be the scoredescribed as a result of the output layerof the machine learning algorithm. In some aspects, the scoremay be accompanied by a description of the score, such as, “obscured”or varying descriptions corresponding to other values of the score. Like the graphical user interfaces previously described, the graphical user interfaceofmay include a region configured to receive a user input confirming that the featurecorrectly identifies the corresponding view F and feature.

9 9 FIGS.A-F 9 9 FIGS.A-F 800 may be displayed to a user while the machine learning algorithmis executed. In this way, the user may see the image corresponding to each view, the identified feature within each image, as well as the score associated with each feature. As previously described, the user may input a confirmation of the displayed data and/or results at varying stages. By displaying the images, features, and scores described with reference toto the user during execution of the deep learning algorithm, the user may be more likely to accept that the final staging value result is accurate and trust the results of the imaging procedure.

10 FIG. 1000 is a diagrammatic view of a graphical user interfacedisplaying ultrasound images, corresponding scores, and a staging value, according to aspects of the present disclosure.

1000 9 9 FIGS.A-F The graphical user interfacemay simultaneously display the six ultrasound images of. Each of these six ultrasound images may correspond to the six views described previously.

1000 1031 909 1031 1031 909 902 9 FIG.A A corresponding list of views, features, and scores is also displayed within the graphical user interface. For example, the labelmay identify the view A and may include a description of the view, including, for example, liver-kidney contrast. The score, described with reference to, is also displayed adjacent to the label. The labeland the scoremay each correspond to the ultrasound image.

1032 918 1032 1032 918 912 1033 928 922 1034 938 932 1035 948 942 1036 958 952 9 FIG.B Similarly, a labelmay identify the view B and may include a description of the view, including, for example, posterior attenuation. The score, described with reference to, is also displayed adjacent to the label. The labeland the scoremay each correspond to the ultrasound image. A labelsimilarly corresponds to the view C and is associated with the scoreand the ultrasound image. A labelcorresponds to the view D and is associated with the scoreand the ultrasound image. A labelcorresponds to the view E and is associated with the scoreand the ultrasound image. A labelcorresponds to the view F and is associated with the scoreand the ultrasound image.

10 FIG. 1050 1052 1052 909 918 928 938 948 958 1052 909 918 928 938 948 958 1052 additionally includes a total score labeland a total score. The total scoremay be a combination of the scores,,,,, andcorresponding to the various views A-F. For example, the total scoremay be a summation of the scores,,,,, and. In some aspects, the total scoremay be a different combination of these scores, such as an average, a median, or any other combination including various relationships or functions.

10 FIG. 3 FIG. 1060 1062 1062 909 918 928 938 948 958 355 300 1062 1052 1052 1052 1062 also includes an NAFLD stage labeland the staging value. As previously described, the staging valuemay be based on the scores,,,,, and. As described with reference to stepof the methodof, the staging valuemay additionally be based on the total score. For example, if the total scorefalls within a certain range of values, this range may correspond to one of the staging values. In the example shown, the total scoremay be 7 and the NAFLD staging valuemay be 3 corresponding to severe progression of NAFLD.

1000 1020 922 210 1000 922 The graphical user interfacemay allow the user to select ultrasound images of the images provided as well as views of the views provided. For example, as shown by the indicator, the user may select the ultrasound image. In response to this selection, the processor circuitmay be configured to modify the graphical user interfaceto highlight the selected ultrasound image.

1033 928 1033 928 922 1070 1033 928 1033 928 922 1033 928 Similarly, the labeland scoremay be highlighted because the labeland the scorecorrespond to the ultrasound image. As additionally shown by the indicator, the user may alternatively select the labelor the score. In response to this selection, the labeland the scoremay be highlighted as well as the ultrasound imagecorresponding to the labeland the score of.

1000 11 FIG. In some aspects, after a user selects an ultrasound image or view within the graphical user interface, the selected ultrasound image may be enlarged as shown and described with reference to.

210 1000 10 FIG. In some aspects, the processor circuitmay additionally be configured to generate a report based on the information displayed in the graphical user interface. The generated report may inform a physician who was not present at the ultrasound imaging procedure of the results. The report may also be viewed by the patient or any other interested parties. The report may include any information, including ultrasound images, overlays, views, features, and scores, as shown and described inas well as anything else shown and described previously.

11 FIG. 1100 922 1062 is a diagrammatic view of a graphical user interfacedisplaying an ultrasound image, corresponding scores, and a stage score, according to aspects of the present disclosure.

1100 1000 1100 922 11 FIG. In some aspects, the graphical user interfacemay be displayed to the user in response to the user selecting an ultrasound image or view of the graphical user interfacedescribed previously. For example, the graphical user interfaceshown inmay correspond to a view displayed to the user after selecting the ultrasound imagecorresponding to the view C.

1100 922 922 924 922 As shown, the graphical user interfacemay include an enlarged view of the ultrasound image. The ultrasound imagemay include the featureidentified within the image. In some aspects, the ultrasound imagemay include additional features, such as various metrics or automatically identified features.

1100 1062 1164 1062 1164 The graphical user interfaceincludes the staging valueas well as a descriptionof the staging value. in the example shown, because the staging valueis 3, the descriptionmay be “severe.”

1100 922 1100 922 100 660 10 FIG. 6 FIG. The graphical user interfacemay provide the user with a more detailed view of the image. A similar graphical user interface may be displayed corresponding to the other ultrasound images or views of. The graphical user interfacemay include any suitable information or detailed data associated with the ultrasound imageincluding any data obtained during the ultrasound imaging procedure. In some aspects, a user of the ultrasound imaging systemmay view, for example, additional ultrasound images associated with the view C, such as any of the imagesdescribed with reference to.

12 FIG. 1 FIG. 1 FIG. 2 FIG. 1200 1200 1200 100 1200 116 134 260 is a flow diagram of a method of determining a staging value of a medical condition, according to aspects of the present disclosure. As illustrated, the methodincludes a number of enumerated steps, but aspects of the methodmay include additional steps before, after, or in between the enumerated steps. In some aspects, one or more of the enumerated steps may be omitted, performed in a different order, or performed concurrently. The steps of the methodcan be carried out by any suitable component within the systemand all steps need not be carried out by the same component. In some aspects, one or more steps of the methodcan be performed by, or at the direction of, a processor circuit, including, e.g., the processor(), the processor(), the processor() or any other suitable component.

1210 1200 4 FIG. At step, the methodincludes outputting, to a display, user guidance to obtain a plurality of ultrasound images corresponding to a plurality of views of the patient anatomy. For example, the user guidance may be of any suitable type, including text or visual graphics, including two-dimensional or three-dimensional images, still images, or animated images. User guidance may also include a reference image and/or a description of the expected dynamic behavior of the acquired ultrasound images in real time. Various aspects of user guidance may include any features shown and described with reference to.

1220 1200 310 300 3 FIG. At step, the methodincludes controlling the transducer array to obtain a first ultrasound image corresponding to a first view of the patient anatomy and a second ultrasound image of the patient anatomy corresponding to a second view of the patient anatomy. Aspects of obtained ultrasound images may be described with reference to stepof the methodof.

1230 1200 7 8 FIGS.- At step, the methodincludes identifying, using a first machine learning algorithm, a first image feature associated with a medical condition of the patient anatomy within the first ultrasound image of the plurality of ultrasound images and a second image feature associated with the medical condition within the second ultrasound image of the plurality of ultrasound images. Image features may be extracted from received ultrasound images in any suitable way. For example, a machine learning network may be configured to extract features from input images, as described in more detail with reference to.

1240 1200 9 9 FIGS.A-F At step, the methodincludes determining, using a second machine learning algorithm, a first sub-score for the first image feature and a second sub-score for the second image feature. The sub-scores assigned to corresponding image features may be of any suitable format or type. Aspects of determining sub-scores associated with identified image features are shown and described with reference to.

1250 1200 355 300 3 FIG. At step, the methodincludes determining a staging value representative of the progression of the medical condition based on the first sub-score and the second sub-score. In some aspects, the staging value may be a staging value of NAFLD progression within a patient. In some aspects, the staging value may be a staging value of another medical condition. Aspects of determining the staging value may be described in greater detail with reference to stepof the methodof.

1260 1200 1260 100 At step, the methodincludes outputting, to the display, a screen display comprising: the staging value and a visual representation of how the staging value was determined, comprising: the first ultrasound image, the indication of the first image feature in the first ultrasound image, and the first sub-score; or the second ultrasound image, the indication of the second image feature in the second ultrasound image, and the second sub-score. In some aspects, the ultrasound images displayed at stepmay be displayed sequentially or simultaneously. By displaying this information to a user of the ultrasound imaging systemduring the imaging procedure, the user may be more confident that the resulting staging value is accurate.

Persons skilled in the art will recognize that the apparatus, systems, and methods described above can be modified in various ways. Accordingly, persons of ordinary skill in the art will appreciate that the aspects encompassed by the present disclosure are not limited to the particular exemplary aspects described above. In that regard, although illustrative aspects have been shown and described, a wide range of modification, change, and substitution is contemplated in the foregoing disclosure. It is understood that such variations may be made to the foregoing without departing from the scope of the present disclosure. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the present disclosure.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” does not exclude the presence of elements or steps other than those listed in a claim. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements.